Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations883
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory106.9 KiB
Average record size in memory124.0 B

Variable types

Text8
Numeric6
Categorical1

Alerts

Overview has unique valuesUnique

Reproduction

Analysis started2024-07-22 23:50:42.688669
Analysis finished2024-07-22 23:51:13.576391
Duration30.89 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct882
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:14.493071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length68
Median length35
Mean length15.16761
Min length2

Characters and Unicode

Total characters13393
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique881 ?
Unique (%)99.8%

Sample

1st rowThe Godfather
2nd rowThe Godfather: Part II
3rd row12 Angry Men
4th rowPulp Fiction
5th rowSchindler's List
ValueCountFrequency (%)
the 217
 
9.0%
of 67
 
2.8%
a 30
 
1.2%
no 24
 
1.0%
la 23
 
1.0%
in 22
 
0.9%
and 20
 
0.8%
de 17
 
0.7%
man 15
 
0.6%
to 15
 
0.6%
Other values (1524) 1962
81.3%
2024-07-22T23:51:16.465919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1529
 
11.4%
e 1236
 
9.2%
a 987
 
7.4%
o 842
 
6.3%
n 811
 
6.1%
i 767
 
5.7%
r 688
 
5.1%
t 633
 
4.7%
h 490
 
3.7%
s 486
 
3.6%
Other values (89) 4924
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1529
 
11.4%
e 1236
 
9.2%
a 987
 
7.4%
o 842
 
6.3%
n 811
 
6.1%
i 767
 
5.7%
r 688
 
5.1%
t 633
 
4.7%
h 490
 
3.7%
s 486
 
3.6%
Other values (89) 4924
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1529
 
11.4%
e 1236
 
9.2%
a 987
 
7.4%
o 842
 
6.3%
n 811
 
6.1%
i 767
 
5.7%
r 688
 
5.1%
t 633
 
4.7%
h 490
 
3.7%
s 486
 
3.6%
Other values (89) 4924
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1529
 
11.4%
e 1236
 
9.2%
a 987
 
7.4%
o 842
 
6.3%
n 811
 
6.1%
i 767
 
5.7%
r 688
 
5.1%
t 633
 
4.7%
h 490
 
3.7%
s 486
 
3.6%
Other values (89) 4924
36.8%

Released_Year
Real number (ℝ)

Distinct99
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1989.4304
Minimum1920
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-07-22T23:51:16.990419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1942
Q11973
median1997
Q32008
95-th percentile2017
Maximum2020
Range100
Interquartile range (IQR)35

Descriptive statistics

Standard deviation23.747406
Coefficient of variation (CV)0.011936787
Kurtosis-0.26003619
Mean1989.4304
Median Absolute Deviation (MAD)15
Skewness-0.83165202
Sum1756667
Variance563.9393
MonotonicityNot monotonic
2024-07-22T23:51:17.660778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2004 28
 
3.2%
2013 25
 
2.8%
2007 24
 
2.7%
2006 23
 
2.6%
2014 23
 
2.6%
2009 22
 
2.5%
2016 22
 
2.5%
1993 21
 
2.4%
2015 21
 
2.4%
2001 21
 
2.4%
Other values (89) 653
74.0%
ValueCountFrequency (%)
1920 1
 
0.1%
1921 1
 
0.1%
1922 1
 
0.1%
1924 1
 
0.1%
1925 2
0.2%
1926 1
 
0.1%
1927 2
0.2%
1928 2
0.2%
1930 1
 
0.1%
1931 3
0.3%
ValueCountFrequency (%)
2020 6
 
0.7%
2019 19
2.2%
2018 12
1.4%
2017 16
1.8%
2016 22
2.5%
2015 21
2.4%
2014 23
2.6%
2013 25
2.8%
2012 18
2.0%
2011 16
1.8%

Certificate
Categorical

Distinct16
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
U
292 
A
182 
R
142 
UA
124 
PG-13
40 
Other values (11)
103 

Length

Max length8
Median length1
Mean length1.6783692
Min length1

Characters and Unicode

Total characters1482
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowA
2nd rowA
3rd rowU
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
U 292
33.1%
A 182
20.6%
R 142
16.1%
UA 124
14.0%
PG-13 40
 
4.5%
PG 37
 
4.2%
Passed 34
 
3.9%
G 11
 
1.2%
Approved 11
 
1.2%
TV-PG 3
 
0.3%
Other values (6) 7
 
0.8%

Length

2024-07-22T23:51:18.349453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
u 292
33.1%
a 182
20.6%
r 142
16.1%
ua 124
14.0%
pg-13 40
 
4.5%
pg 37
 
4.2%
passed 34
 
3.9%
g 11
 
1.2%
approved 11
 
1.2%
tv-pg 3
 
0.3%
Other values (6) 7
 
0.8%

Most occurring characters

ValueCountFrequency (%)
U 418
28.2%
A 319
21.5%
R 142
 
9.6%
P 116
 
7.8%
G 93
 
6.3%
s 68
 
4.6%
e 46
 
3.1%
d 46
 
3.1%
- 45
 
3.0%
1 42
 
2.8%
Other values (14) 147
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 418
28.2%
A 319
21.5%
R 142
 
9.6%
P 116
 
7.8%
G 93
 
6.3%
s 68
 
4.6%
e 46
 
3.1%
d 46
 
3.1%
- 45
 
3.0%
1 42
 
2.8%
Other values (14) 147
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 418
28.2%
A 319
21.5%
R 142
 
9.6%
P 116
 
7.8%
G 93
 
6.3%
s 68
 
4.6%
e 46
 
3.1%
d 46
 
3.1%
- 45
 
3.0%
1 42
 
2.8%
Other values (14) 147
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 418
28.2%
A 319
21.5%
R 142
 
9.6%
P 116
 
7.8%
G 93
 
6.3%
s 68
 
4.6%
e 46
 
3.1%
d 46
 
3.1%
- 45
 
3.0%
1 42
 
2.8%
Other values (14) 147
 
9.9%

Runtime
Real number (ℝ)

Distinct138
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.9966
Minimum45
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:18.901491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile87
Q1102
median118
Q3135
95-th percentile176.8
Maximum321
Range276
Interquartile range (IQR)33

Descriptive statistics

Standard deviation28.111718
Coefficient of variation (CV)0.23043034
Kurtosis3.8048169
Mean121.9966
Median Absolute Deviation (MAD)16
Skewness1.2778454
Sum107723
Variance790.2687
MonotonicityNot monotonic
2024-07-22T23:51:21.831634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 22
 
2.5%
129 21
 
2.4%
101 21
 
2.4%
110 19
 
2.2%
122 18
 
2.0%
100 18
 
2.0%
130 18
 
2.0%
120 17
 
1.9%
102 16
 
1.8%
118 16
 
1.8%
Other values (128) 697
78.9%
ValueCountFrequency (%)
45 1
 
0.1%
64 1
 
0.1%
67 1
 
0.1%
68 1
 
0.1%
69 1
 
0.1%
70 1
 
0.1%
71 2
0.2%
72 2
0.2%
75 2
0.2%
76 3
0.3%
ValueCountFrequency (%)
321 1
0.1%
242 1
0.1%
229 1
0.1%
228 1
0.1%
224 1
0.1%
220 1
0.1%
212 1
0.1%
210 1
0.1%
209 1
0.1%
207 1
0.1%

Genre
Text

Distinct196
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:22.502725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length24
Mean length18.566251
Min length5

Characters and Unicode

Total characters16394
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)8.7%

Sample

1st rowCrime, Drama
2nd rowCrime, Drama
3rd rowCrime, Drama
4th rowCrime, Drama
5th rowBiography, Drama, History
ValueCountFrequency (%)
drama 683
30.8%
comedy 207
 
9.3%
crime 202
 
9.1%
action 134
 
6.0%
thriller 125
 
5.6%
romance 120
 
5.4%
adventure 119
 
5.4%
biography 102
 
4.6%
mystery 93
 
4.2%
animation 55
 
2.5%
Other values (11) 380
17.1%
2024-07-22T23:51:23.455994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1859
 
11.3%
r 1695
 
10.3%
, 1337
 
8.2%
1337
 
8.2%
m 1331
 
8.1%
e 1019
 
6.2%
i 952
 
5.8%
o 767
 
4.7%
D 683
 
4.2%
y 644
 
3.9%
Other values (23) 4770
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1859
 
11.3%
r 1695
 
10.3%
, 1337
 
8.2%
1337
 
8.2%
m 1331
 
8.1%
e 1019
 
6.2%
i 952
 
5.8%
o 767
 
4.7%
D 683
 
4.2%
y 644
 
3.9%
Other values (23) 4770
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1859
 
11.3%
r 1695
 
10.3%
, 1337
 
8.2%
1337
 
8.2%
m 1331
 
8.1%
e 1019
 
6.2%
i 952
 
5.8%
o 767
 
4.7%
D 683
 
4.2%
y 644
 
3.9%
Other values (23) 4770
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1859
 
11.3%
r 1695
 
10.3%
, 1337
 
8.2%
1337
 
8.2%
m 1331
 
8.1%
e 1019
 
6.2%
i 952
 
5.8%
o 767
 
4.7%
D 683
 
4.2%
y 644
 
3.9%
Other values (23) 4770
29.1%

IMDB_Rating
Real number (ℝ)

Distinct16
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9382786
Minimum7.6
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:24.210217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q17.7
median7.9
Q38.1
95-th percentile8.4
Maximum9.2
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.26022444
Coefficient of variation (CV)0.032780966
Kurtosis1.0793163
Mean7.9382786
Median Absolute Deviation (MAD)0.2
Skewness0.89377459
Sum7009.5
Variance0.067716761
MonotonicityDecreasing
2024-07-22T23:51:25.027194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.7 142
16.1%
7.8 135
15.3%
8 120
13.6%
8.1 116
13.1%
7.6 109
12.3%
7.9 98
11.1%
8.2 62
7.0%
8.3 42
 
4.8%
8.4 23
 
2.6%
8.5 15
 
1.7%
Other values (6) 21
 
2.4%
ValueCountFrequency (%)
7.6 109
12.3%
7.7 142
16.1%
7.8 135
15.3%
7.9 98
11.1%
8 120
13.6%
8.1 116
13.1%
8.2 62
7.0%
8.3 42
 
4.8%
8.4 23
 
2.6%
8.5 15
 
1.7%
ValueCountFrequency (%)
9.2 1
 
0.1%
9 2
 
0.2%
8.9 2
 
0.2%
8.8 2
 
0.2%
8.7 2
 
0.2%
8.6 12
 
1.4%
8.5 15
 
1.7%
8.4 23
 
2.6%
8.3 42
4.8%
8.2 62
7.0%

Overview
Text

UNIQUE 

Distinct883
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:26.535679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length313
Median length198
Mean length144.73839
Min length40

Characters and Unicode

Total characters127804
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique883 ?
Unique (%)100.0%

Sample

1st rowAn organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.
2nd rowThe early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.
3rd rowA jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.
4th rowThe lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.
5th rowIn German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.
ValueCountFrequency (%)
a 1423
 
6.5%
the 1019
 
4.7%
of 689
 
3.2%
to 674
 
3.1%
and 602
 
2.8%
in 518
 
2.4%
his 454
 
2.1%
an 259
 
1.2%
is 215
 
1.0%
with 207
 
0.9%
Other values (5392) 15770
72.2%
2024-07-22T23:51:29.203046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20961
16.4%
e 12164
 
9.5%
a 8527
 
6.7%
t 8086
 
6.3%
i 7833
 
6.1%
n 7555
 
5.9%
o 7446
 
5.8%
r 7146
 
5.6%
s 7028
 
5.5%
h 4902
 
3.8%
Other values (75) 36156
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20961
16.4%
e 12164
 
9.5%
a 8527
 
6.7%
t 8086
 
6.3%
i 7833
 
6.1%
n 7555
 
5.9%
o 7446
 
5.8%
r 7146
 
5.6%
s 7028
 
5.5%
h 4902
 
3.8%
Other values (75) 36156
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20961
16.4%
e 12164
 
9.5%
a 8527
 
6.7%
t 8086
 
6.3%
i 7833
 
6.1%
n 7555
 
5.9%
o 7446
 
5.8%
r 7146
 
5.6%
s 7028
 
5.5%
h 4902
 
3.8%
Other values (75) 36156
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20961
16.4%
e 12164
 
9.5%
a 8527
 
6.7%
t 8086
 
6.3%
i 7833
 
6.1%
n 7555
 
5.9%
o 7446
 
5.8%
r 7146
 
5.6%
s 7028
 
5.5%
h 4902
 
3.8%
Other values (75) 36156
28.3%

Meta_score
Real number (ℝ)

Distinct64
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.055665
Minimum28
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:29.955739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile57
Q172
median77.969121
Q386
95-th percentile96
Maximum100
Range72
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.474185
Coefficient of variation (CV)0.14700003
Kurtosis1.1875866
Mean78.055665
Median Absolute Deviation (MAD)6.9691211
Skewness-0.69983997
Sum68923.152
Variance131.65693
MonotonicityNot monotonic
2024-07-22T23:51:30.431859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.96912114 157
 
17.8%
76 29
 
3.3%
84 26
 
2.9%
90 26
 
2.9%
86 25
 
2.8%
77 24
 
2.7%
85 23
 
2.6%
73 23
 
2.6%
83 23
 
2.6%
72 22
 
2.5%
Other values (54) 505
57.2%
ValueCountFrequency (%)
28 1
 
0.1%
30 1
 
0.1%
33 1
 
0.1%
36 1
 
0.1%
40 1
 
0.1%
41 1
 
0.1%
44 1
 
0.1%
45 3
0.3%
46 1
 
0.1%
47 4
0.5%
ValueCountFrequency (%)
100 12
1.4%
99 4
 
0.5%
98 9
1.0%
97 11
1.2%
96 16
1.8%
95 8
0.9%
94 17
1.9%
93 14
1.6%
92 11
1.2%
91 17
1.9%
Distinct503
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:31.438351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length22
Mean length13.523216
Min length7

Characters and Unicode

Total characters11941
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique333 ?
Unique (%)37.7%

Sample

1st rowFrancis Ford Coppola
2nd rowFrancis Ford Coppola
3rd rowSidney Lumet
4th rowQuentin Tarantino
5th rowSteven Spielberg
ValueCountFrequency (%)
john 31
 
1.7%
david 23
 
1.3%
james 17
 
0.9%
robert 16
 
0.9%
martin 15
 
0.8%
alfred 14
 
0.8%
stanley 14
 
0.8%
hitchcock 14
 
0.8%
richard 14
 
0.8%
george 13
 
0.7%
Other values (829) 1666
90.7%
2024-07-22T23:51:32.730924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1042
 
8.7%
a 1029
 
8.6%
954
 
8.0%
n 845
 
7.1%
r 802
 
6.7%
o 753
 
6.3%
i 751
 
6.3%
l 489
 
4.1%
s 422
 
3.5%
t 363
 
3.0%
Other values (59) 4491
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1042
 
8.7%
a 1029
 
8.6%
954
 
8.0%
n 845
 
7.1%
r 802
 
6.7%
o 753
 
6.3%
i 751
 
6.3%
l 489
 
4.1%
s 422
 
3.5%
t 363
 
3.0%
Other values (59) 4491
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1042
 
8.7%
a 1029
 
8.6%
954
 
8.0%
n 845
 
7.1%
r 802
 
6.7%
o 753
 
6.3%
i 751
 
6.3%
l 489
 
4.1%
s 422
 
3.5%
t 363
 
3.0%
Other values (59) 4491
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1042
 
8.7%
a 1029
 
8.6%
954
 
8.0%
n 845
 
7.1%
r 802
 
6.7%
o 753
 
6.3%
i 751
 
6.3%
l 489
 
4.1%
s 422
 
3.5%
t 363
 
3.0%
Other values (59) 4491
37.6%

Star1
Text

Distinct610
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:33.680987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length21
Mean length13.062288
Min length4

Characters and Unicode

Total characters11534
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique477 ?
Unique (%)54.0%

Sample

1st rowMarlon Brando
2nd rowAl Pacino
3rd rowHenry Fonda
4th rowJohn Travolta
5th rowLiam Neeson
ValueCountFrequency (%)
khan 16
 
0.9%
james 15
 
0.8%
robert 14
 
0.8%
tom 14
 
0.8%
john 13
 
0.7%
ethan 11
 
0.6%
niro 11
 
0.6%
de 11
 
0.6%
al 11
 
0.6%
michael 11
 
0.6%
Other values (1046) 1679
93.0%
2024-07-22T23:51:35.149070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1115
 
9.7%
e 979
 
8.5%
923
 
8.0%
n 849
 
7.4%
r 725
 
6.3%
i 691
 
6.0%
o 667
 
5.8%
l 514
 
4.5%
t 415
 
3.6%
h 386
 
3.3%
Other values (62) 4270
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1115
 
9.7%
e 979
 
8.5%
923
 
8.0%
n 849
 
7.4%
r 725
 
6.3%
i 691
 
6.0%
o 667
 
5.8%
l 514
 
4.5%
t 415
 
3.6%
h 386
 
3.3%
Other values (62) 4270
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1115
 
9.7%
e 979
 
8.5%
923
 
8.0%
n 849
 
7.4%
r 725
 
6.3%
i 691
 
6.0%
o 667
 
5.8%
l 514
 
4.5%
t 415
 
3.6%
h 386
 
3.3%
Other values (62) 4270
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1115
 
9.7%
e 979
 
8.5%
923
 
8.0%
n 849
 
7.4%
r 725
 
6.3%
i 691
 
6.0%
o 667
 
5.8%
l 514
 
4.5%
t 415
 
3.6%
h 386
 
3.3%
Other values (62) 4270
37.0%

Star2
Text

Distinct772
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:35.736588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length22
Mean length13.218573
Min length4

Characters and Unicode

Total characters11672
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique685 ?
Unique (%)77.6%

Sample

1st rowAl Pacino
2nd rowRobert De Niro
3rd rowLee J. Cobb
4th rowUma Thurman
5th rowRalph Fiennes
ValueCountFrequency (%)
john 20
 
1.1%
robert 12
 
0.7%
michael 12
 
0.7%
lee 10
 
0.6%
james 9
 
0.5%
george 7
 
0.4%
jack 7
 
0.4%
julie 7
 
0.4%
diane 6
 
0.3%
keaton 6
 
0.3%
Other values (1300) 1720
94.7%
2024-07-22T23:51:36.617038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1196
 
10.2%
e 1048
 
9.0%
933
 
8.0%
n 847
 
7.3%
r 783
 
6.7%
i 721
 
6.2%
o 625
 
5.4%
l 517
 
4.4%
t 418
 
3.6%
s 381
 
3.3%
Other values (59) 4203
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1196
 
10.2%
e 1048
 
9.0%
933
 
8.0%
n 847
 
7.3%
r 783
 
6.7%
i 721
 
6.2%
o 625
 
5.4%
l 517
 
4.4%
t 418
 
3.6%
s 381
 
3.3%
Other values (59) 4203
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1196
 
10.2%
e 1048
 
9.0%
933
 
8.0%
n 847
 
7.3%
r 783
 
6.7%
i 721
 
6.2%
o 625
 
5.4%
l 517
 
4.4%
t 418
 
3.6%
s 381
 
3.3%
Other values (59) 4203
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1196
 
10.2%
e 1048
 
9.0%
933
 
8.0%
n 847
 
7.3%
r 783
 
6.7%
i 721
 
6.2%
o 625
 
5.4%
l 517
 
4.4%
t 418
 
3.6%
s 381
 
3.3%
Other values (59) 4203
36.0%

Star3
Text

Distinct800
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:37.231509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length27
Median length21
Mean length13.355606
Min length4

Characters and Unicode

Total characters11793
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique734 ?
Unique (%)83.1%

Sample

1st rowJames Caan
2nd rowRobert Duvall
3rd rowMartin Balsam
4th rowSamuel L. Jackson
5th rowBen Kingsley
ValueCountFrequency (%)
john 21
 
1.2%
robert 15
 
0.8%
michael 13
 
0.7%
richard 8
 
0.4%
jack 8
 
0.4%
christopher 8
 
0.4%
george 7
 
0.4%
james 7
 
0.4%
de 6
 
0.3%
lee 6
 
0.3%
Other values (1340) 1723
94.6%
2024-07-22T23:51:38.096135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1158
 
9.8%
e 1010
 
8.6%
939
 
8.0%
n 825
 
7.0%
i 802
 
6.8%
r 747
 
6.3%
o 681
 
5.8%
l 545
 
4.6%
t 387
 
3.3%
s 374
 
3.2%
Other values (63) 4325
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1158
 
9.8%
e 1010
 
8.6%
939
 
8.0%
n 825
 
7.0%
i 802
 
6.8%
r 747
 
6.3%
o 681
 
5.8%
l 545
 
4.6%
t 387
 
3.3%
s 374
 
3.2%
Other values (63) 4325
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1158
 
9.8%
e 1010
 
8.6%
939
 
8.0%
n 825
 
7.0%
i 802
 
6.8%
r 747
 
6.3%
o 681
 
5.8%
l 545
 
4.6%
t 387
 
3.3%
s 374
 
3.2%
Other values (63) 4325
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1158
 
9.8%
e 1010
 
8.6%
939
 
8.0%
n 825
 
7.0%
i 802
 
6.8%
r 747
 
6.3%
o 681
 
5.8%
l 545
 
4.6%
t 387
 
3.3%
s 374
 
3.2%
Other values (63) 4325
36.7%

Star4
Text

Distinct838
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:38.668053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length27
Median length22
Mean length13.244621
Min length4

Characters and Unicode

Total characters11695
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique794 ?
Unique (%)89.9%

Sample

1st rowDiane Keaton
2nd rowDiane Keaton
3rd rowJohn Fiedler
4th rowBruce Willis
5th rowCaroline Goodall
ValueCountFrequency (%)
john 21
 
1.1%
michael 12
 
0.7%
james 10
 
0.5%
bill 7
 
0.4%
charles 7
 
0.4%
kim 7
 
0.4%
paul 6
 
0.3%
martin 6
 
0.3%
mitchell 6
 
0.3%
jack 6
 
0.3%
Other values (1430) 1739
95.2%
2024-07-22T23:51:39.512475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1159
 
9.9%
e 978
 
8.4%
944
 
8.1%
r 807
 
6.9%
n 796
 
6.8%
i 763
 
6.5%
o 628
 
5.4%
l 550
 
4.7%
s 390
 
3.3%
h 367
 
3.1%
Other values (63) 4313
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1159
 
9.9%
e 978
 
8.4%
944
 
8.1%
r 807
 
6.9%
n 796
 
6.8%
i 763
 
6.5%
o 628
 
5.4%
l 550
 
4.7%
s 390
 
3.3%
h 367
 
3.1%
Other values (63) 4313
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1159
 
9.9%
e 978
 
8.4%
944
 
8.1%
r 807
 
6.9%
n 796
 
6.8%
i 763
 
6.5%
o 628
 
5.4%
l 550
 
4.7%
s 390
 
3.3%
h 367
 
3.1%
Other values (63) 4313
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1159
 
9.9%
e 978
 
8.4%
944
 
8.1%
r 807
 
6.9%
n 796
 
6.8%
i 763
 
6.5%
o 628
 
5.4%
l 550
 
4.7%
s 390
 
3.3%
h 367
 
3.1%
Other values (63) 4313
36.9%

No_of_Votes
Real number (ℝ)

Distinct882
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211144.59
Minimum25088
Maximum1854740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:39.960494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25088
5-th percentile28755.1
Q151337.5
median108399
Q3265380
95-th percentile720163.5
Maximum1854740
Range1829652
Interquartile range (IQR)214042.5

Descriptive statistics

Standard deviation251736.7
Coefficient of variation (CV)1.192248
Kurtosis7.8170685
Mean211144.59
Median Absolute Deviation (MAD)70332
Skewness2.457641
Sum1.8644067 × 108
Variance6.3371368 × 1010
MonotonicityNot monotonic
2024-07-22T23:51:40.468437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65341 2
 
0.2%
1620367 1
 
0.1%
148359 1
 
0.1%
42376 1
 
0.1%
126082 1
 
0.1%
184740 1
 
0.1%
125276 1
 
0.1%
124193 1
 
0.1%
122779 1
 
0.1%
45624 1
 
0.1%
Other values (872) 872
98.8%
ValueCountFrequency (%)
25088 1
0.1%
25198 1
0.1%
25229 1
0.1%
25312 1
0.1%
25344 1
0.1%
25938 1
0.1%
26337 1
0.1%
26402 1
0.1%
26429 1
0.1%
26457 1
0.1%
ValueCountFrequency (%)
1854740 1
0.1%
1826188 1
0.1%
1620367 1
0.1%
1445096 1
0.1%
1270197 1
0.1%
1267869 1
0.1%
1213505 1
0.1%
1190259 1
0.1%
1189773 1
0.1%
1187498 1
0.1%

Gross
Real number (ℝ)

Distinct707
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39002905
Minimum1305
Maximum1.6119778 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-07-22T23:51:40.904598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1305
5-th percentile152301.9
Q13990500
median28262574
Q368082574
95-th percentile1.167761 × 108
Maximum1.6119778 × 108
Range1.6119648 × 108
Interquartile range (IQR)64092074

Descriptive statistics

Standard deviation38044616
Coefficient of variation (CV)0.97543032
Kurtosis0.051466549
Mean39002905
Median Absolute Deviation (MAD)27262529
Skewness0.84905883
Sum3.4439565 × 1010
Variance1.4473928 × 1015
MonotonicityNot monotonic
2024-07-22T23:51:41.424747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68082574.1 169
 
19.1%
4360000 5
 
0.6%
5321508 2
 
0.2%
9600000 2
 
0.2%
5450000 2
 
0.2%
25000000 2
 
0.2%
2500000 1
 
0.1%
70136369 1
 
0.1%
1436000 1
 
0.1%
61503218 1
 
0.1%
Other values (697) 697
78.9%
ValueCountFrequency (%)
1305 1
0.1%
3296 1
0.1%
3600 1
0.1%
6013 1
0.1%
6460 1
0.1%
7461 1
0.1%
8060 1
0.1%
10177 1
0.1%
10950 1
0.1%
12562 1
0.1%
ValueCountFrequency (%)
161197785 1
0.1%
159600000 1
0.1%
159227644 1
0.1%
156452370 1
0.1%
154058340 1
0.1%
151101803 1
0.1%
148478011 1
0.1%
148095302 1
0.1%
146408305 1
0.1%
145000989 1
0.1%

Interactions

2024-07-22T23:51:05.103778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:44.065131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:48.446702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:51.480588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:56.835251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:00.862457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:05.685894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:44.670106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:48.887870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:52.308190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:57.293496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:01.391101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:06.628175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:45.162047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:49.271718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:53.354923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:57.773305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:01.899282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:07.660329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:46.103950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:49.693877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:54.300793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:58.543524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:02.716404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:08.578024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:46.968499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:50.302435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:55.107877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:59.316249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:03.438400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:09.441573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:47.793421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:50.714627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:50:56.143281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:00.060690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T23:51:04.177945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-22T23:51:41.808324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CertificateGrossIMDB_RatingMeta_scoreNo_of_VotesReleased_YearRuntime
Certificate1.0000.1010.0300.0600.0890.2580.109
Gross0.1011.000-0.067-0.0410.283-0.0220.088
IMDB_Rating0.030-0.0671.0000.2450.174-0.1360.212
Meta_score0.060-0.0410.2451.000-0.038-0.232-0.050
No_of_Votes0.0890.2830.174-0.0381.0000.1960.122
Released_Year0.258-0.022-0.136-0.2320.1961.0000.199
Runtime0.1090.0880.212-0.0500.1220.1991.000

Missing values

2024-07-22T23:51:10.828424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-22T23:51:12.822611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Series_TitleReleased_YearCertificateRuntimeGenreIMDB_RatingOverviewMeta_scoreDirectorStar1Star2Star3Star4No_of_VotesGross
0The Godfather1972A175.0Crime, Drama9.2An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.100.0Francis Ford CoppolaMarlon BrandoAl PacinoJames CaanDiane Keaton16203671.349664e+08
2The Godfather: Part II1974A202.0Crime, Drama9.0The early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.90.0Francis Ford CoppolaAl PacinoRobert De NiroRobert DuvallDiane Keaton11299525.730000e+07
312 Angry Men1957U96.0Crime, Drama9.0A jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.96.0Sidney LumetHenry FondaLee J. CobbMartin BalsamJohn Fiedler6898454.360000e+06
5Pulp Fiction1994A154.0Crime, Drama8.9The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.94.0Quentin TarantinoJohn TravoltaUma ThurmanSamuel L. JacksonBruce Willis18261881.079288e+08
6Schindler's List1993A195.0Biography, Drama, History8.9In German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.94.0Steven SpielbergLiam NeesonRalph FiennesBen KingsleyCaroline Goodall12135059.689882e+07
8Fight Club1999A139.0Drama8.8An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.66.0David FincherBrad PittEdward NortonMeat LoafZach Grenier18547403.703010e+07
11Il buono, il brutto, il cattivo1966A161.0Western8.8A bounty hunting scam joins two men in an uneasy alliance against a third in a race to find a fortune in gold buried in a remote cemetery.90.0Sergio LeoneClint EastwoodEli WallachLee Van CleefAldo Giuffrè6883906.100000e+06
14Goodfellas1990A146.0Biography, Crime, Drama8.7The story of Henry Hill and his life in the mob, covering his relationship with his wife Karen Hill and his mob partners Jimmy Conway and Tommy DeVito in the Italian-American crime syndicate.90.0Martin ScorseseRobert De NiroRay LiottaJoe PesciLorraine Bracco10207274.683639e+07
16One Flew Over the Cuckoo's Nest1975A133.0Drama8.7A criminal pleads insanity and is admitted to a mental institution, where he rebels against the oppressive nurse and rallies up the scared patients.83.0Milos FormanJack NicholsonLouise FletcherMichael BerrymanPeter Brocco9180881.120000e+08
17Hamilton2020PG-13160.0Biography, Drama, History8.6The real life of one of America's foremost founding fathers and first Secretary of the Treasury, Alexander Hamilton. Captured live on Broadway from the Richard Rodgers Theater with the original Broadway cast.90.0Thomas KailLin-Manuel MirandaPhillipa SooLeslie Odom Jr.Renée Elise Goldsberry552916.808257e+07
Series_TitleReleased_YearCertificateRuntimeGenreIMDB_RatingOverviewMeta_scoreDirectorStar1Star2Star3Star4No_of_VotesGross
989Giù la testa1971PG157.0Drama, War, Western7.6A low-life bandit and an I.R.A. explosives expert rebel against the government and become heroes of the Mexican Revolution.77.0Sergio LeoneRod SteigerJames CoburnRomolo ValliMaria Monti301446.966900e+05
990Kelly's Heroes1970GP144.0Adventure, Comedy, War7.6A group of U.S. soldiers sneaks across enemy lines to get their hands on a secret stash of Nazi treasure.50.0Brian G. HuttonClint EastwoodTelly SavalasDon RicklesCarroll O'Connor453381.378435e+06
991The Jungle Book1967U78.0Animation, Adventure, Family7.6Bagheera the Panther and Baloo the Bear have a difficult time trying to convince a boy to leave the jungle for human civilization.65.0Wolfgang ReithermanPhil HarrisSebastian CabotLouis PrimaBruce Reitherman1664091.418436e+08
992Blowup1966A111.0Drama, Mystery, Thriller7.6A fashion photographer unknowingly captures a death on film after following two lovers in a park.82.0Michelangelo AntonioniDavid HemmingsVanessa RedgraveSarah MilesJohn Castle565136.808257e+07
993A Hard Day's Night1964U87.0Comedy, Music, Musical7.6Over two "typical" days in the life of The Beatles, the boys struggle to keep themselves and Sir Paul McCartney's mischievous grandfather in check while preparing for a live television performance.96.0Richard LesterJohn LennonPaul McCartneyGeorge HarrisonRingo Starr403511.378002e+07
994Breakfast at Tiffany's1961A115.0Comedy, Drama, Romance7.6A young New York socialite becomes interested in a young man who has moved into her apartment building, but her past threatens to get in the way.76.0Blake EdwardsAudrey HepburnGeorge PeppardPatricia NealBuddy Ebsen1665446.808257e+07
995Giant1956G201.0Drama, Western7.6Sprawling epic covering the life of a Texas cattle rancher and his family and associates.84.0George StevensElizabeth TaylorRock HudsonJames DeanCarroll Baker340756.808257e+07
996From Here to Eternity1953Passed118.0Drama, Romance, War7.6In Hawaii in 1941, a private is cruelly punished for not boxing on his unit's team, while his captain's wife and second-in-command are falling in love.85.0Fred ZinnemannBurt LancasterMontgomery CliftDeborah KerrDonna Reed433743.050000e+07
997Lifeboat1944U97.0Drama, War7.6Several survivors of a torpedoed merchant ship in World War II find themselves in the same lifeboat with one of the crew members of the U-boat that sank their ship.78.0Alfred HitchcockTallulah BankheadJohn HodiakWalter SlezakWilliam Bendix264716.808257e+07
998The 39 Steps1935U86.0Crime, Mystery, Thriller7.6A man in London tries to help a counter-espionage Agent. But when the Agent is killed, and the man stands accused, he must go on the run to save himself and stop a spy ring which is trying to steal top secret information.93.0Alfred HitchcockRobert DonatMadeleine CarrollLucie MannheimGodfrey Tearle518536.808257e+07